A vehicle safety warning automatic control method, system, and storage medium
By using in-vehicle cameras and embedded models to identify sharp bends and horn-sounding signs, the system automatically controls the vehicle to sound its horn as a warning, solving the problem of inaccurate recognition during driving and improving driving safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN DESAY SV AUTOMOTIVE CO LTD
- Filing Date
- 2023-02-24
- Publication Date
- 2026-07-03
AI Technical Summary
In existing technologies, vehicles cannot effectively identify sharp bends and horn signs while driving, leading to inaccurate judgments by drivers and making them prone to violating traffic regulations or causing accidents.
By acquiring image information through the vehicle's front-facing camera and utilizing embedded target recognition and distance calculation models, the system automatically controls the vehicle to sound its horn to warn oncoming traffic, thereby reducing the occurrence of accidents.
It enables automatic recognition of sharp bends and horn signs during driving, reducing disorderly horn use, improving driving safety, and lowering the accident rate.
Smart Images
Figure CN116279555B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent vehicle technology, and more specifically, to an automatic control method, system, and storage medium for vehicle safety warnings. Background Technology
[0002] With the development of vehicle intelligence and the improvement of the performance of on-board domain controllers, more and more sensors and detection algorithms are being integrated into vehicles to realize driver assistance functions. The number of vehicles in society has increased dramatically, and coupled with sudden weather and human factors, traffic accidents occur frequently. When driving on roads, drivers generally rely solely on their own observation. Due to the influence of factors such as driving visibility and traffic environment, inaccurate judgments of road conditions often occur, leading to driving violations. These violations can range from minor offenses such as traffic violations resulting in points deductions and fines to serious accidents that affect driving safety. Therefore, there is an urgent need to provide a safety warning method to reduce the occurrence of accidents. Summary of the Invention
[0003] In view of this, the purpose of the present invention is to provide a vehicle safety warning automatic control method, system and storage medium that can detect when a vehicle is on a sharp bend and when a warning sign is in use, and automatically control the vehicle to issue a safety warning to remind oncoming vehicles, thereby reducing the occurrence of accidents.
[0004] To achieve the above technical objectives, the present invention provides the following technical solution:
[0005] An automatic control method for vehicle safety warnings, the method comprising:
[0006] S100: When the vehicle is in motion, acquire the forward image information of the current vehicle.
[0007] S200: When a warning target is detected in the image information, the vehicle warning sound control program is entered; the warning target includes at least: sharp bends and horn signs.
[0008] S300: Calculate the distance between the current vehicle and the warning target.
[0009] S400: When the distance meets the preset conditions, control the current vehicle to issue a safety warning according to the preset rules.
[0010] In the above technical solution, the vehicle's front-facing camera acquires the image information of the vehicle's front, an embedded target recognition model identifies whether a warning target exists in the image information, an embedded distance calculation model calculates the distance between the vehicle and the warning target, and a judgment module judges the distance between the vehicle and the warning target. The judgment module can be connected to the vehicle's warning sound system. When the vehicle is detected to be on a sharp bend with a horn sign, the algorithm automatically controls the vehicle's warning sound system to sound the horn to remind oncoming vehicles, thereby reducing the occurrence of accidents.
[0011] Preferably, step S200 specifically includes:
[0012] S201: Extract the target region from the image information using a warning target recognition model; the target region includes at least: traffic sign region and road region.
[0013] S202: Identify the target area. If the warning target exists, proceed to the vehicle warning sound control program; otherwise, return to S100.
[0014] In the above technical solution, the warning target recognition model is a neural network, which is a type of supervised learning. The characteristic of supervised learning is that each sample in the training sample set has a corresponding label, that is, each sample has its own corresponding category. The result obtained by the activation function of the neural network is actually the probability of which category the feature data belongs to. For example, if the training sample set has two categories: horn signs and sharp bends, after inputting the feature data into the neural network, the resulting category and the probability corresponding to the category are the probability of a certain horn sign and the probability of a certain sharp bend, respectively.
[0015] To understand this further, a sharp bend can be a road section with an angle greater than or equal to 90 degrees.
[0016] The extraction module in the warning target recognition module segments the acquired image information to extract the traffic sign area and road area, and then the recognition module identifies the target area to obtain the warning target.
[0017] Preferably, step S300 specifically includes:
[0018] S301: Extract the location information of the warning target through a distance calculation model; the location information includes at least: road width information, vehicle and camera angle information, and vehicle size information.
[0019] S302: Calculate the location information to obtain the first distance L1 between the current vehicle and the sharp bend road condition, and the second distance L2 between the current vehicle and the horn sign.
[0020] In the above technical solution, the distance calculation model and the warning target recognition model are both neural networks. The relevant principles will not be elaborated here. It should be understood that the angle information between the vehicle and the camera can be obtained through the vehicle body level sensor.
[0021] Preferably, step S400 specifically includes:
[0022] S401: The vehicle safety warning sign F is the first sign.
[0023] S402: Set the first threshold L1' and the second threshold L2'.
[0024] S403: Determine whether the first distance L1 is less than the first threshold L1'. If so, trigger the vehicle safety warning function and set the reminder sign F to the second sign; otherwise, proceed to S402.
[0025] S404: Determine whether the second distance L2 is less than the second threshold L2'. If so, trigger the vehicle safety warning function and set the reminder flag F to the second flag; otherwise, return to S401. Wherein, L1' and L2' are real numbers greater than zero.
[0026] Preferably, step S400 further includes:
[0027] When the warning sign F is the second sign, the vehicle horn warning function will no longer be triggered, and steps S200 to S300 will be repeated.
[0028] When the reminder flag F is the second flag, and the warning target is not identified within a preset time Ts, the reminder flag is set to the first flag; where T is a real number greater than zero.
[0029] In the above technical solution, the automatic start-stop function of the vehicle's warning sound system (or other safety warning system) can be set. For example, when the detection algorithm identifies a horn sign and a sharp curve during driving, the horn warning function is triggered, and the warning sign is set to the second sign. When F is the second sign and a specified time Ts has elapsed, the warning sign F is set to the first sign, and the vehicle enters the next horn detection waiting state. Furthermore, during the period when F is the second sign, the automatic horn will not be triggered again, and steps S200 to S300 will be repeated. If no sharp curve or horn sign is identified within the specified time Ts, the warning sign F will be set to the first sign.
[0030] Preferably, before step S100, the method further includes:
[0031] The warning target recognition model is obtained by training a neural network using data from horn signage and sharp bend road conditions.
[0032] The distance calculation model is obtained by training a neural network using road width data, vehicle and camera angle data, and vehicle size data.
[0033] As another preferred embodiment, the present invention also provides a system for automatic control of vehicle safety warnings, the system comprising at least:
[0034] The acquisition module is used to acquire the current image information of the vehicle in front when the vehicle is in motion.
[0035] The warning target recognition module is used to identify warning targets in the image information.
[0036] The distance calculation module is used to identify the distance between the current vehicle and the warning target.
[0037] The judgment module is used to determine whether the distance meets the preset conditions and control the current vehicle to sound its horn according to the preset rules.
[0038] Preferably, the warning target recognition module includes at least: a first extraction module for extracting a target region from the image information; and a recognition module for recognizing the target region to obtain the warning target.
[0039] Preferably, the distance calculation module includes at least: a second extraction module for extracting the location information of the warning target; and a calculation module for calculating the location information to obtain a first distance L1 between the current vehicle and the sharp bend road condition, and a second distance L2 between the current vehicle and the horn sign.
[0040] Preferably, the acquisition module is connected to the vehicle-mounted front camera; the distance calculation module is connected to the vehicle level sensor, which is used to acquire the angle information between the vehicle and the camera; the judgment module is connected to the vehicle-mounted safety warning system, which is equipped with a safety warning function; the acquisition module, the warning target recognition module, the distance calculation module, and the judgment module are embedded in the vehicle's infotainment system.
[0041] As another preferred embodiment, the present invention also provides a storage medium located in any control unit, the storage medium comprising a computer program executable by a processor, the computer program being used to perform the vehicle safety warning automatic control method as described above.
[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0043] This invention can identify sharp curves and horn-sounding signs during driving, calculate the distance between the current vehicle and the sharp curve or horn-sounding sign, and determine whether horn honking is necessary based on the calculation results. It can automatically control the start and stop of the horn-sounding prompt function, avoid disorderly horn honking, enrich the application scenarios of perception algorithms, reduce accidents caused by sharp curves, and improve driving safety. Attached Figure Description
[0044] Figure 1 This is a flowchart of an automatic control method for vehicle safety warnings according to the present invention.
[0045] Figure 2 This is a flowchart of an automatic control method for vehicle safety warnings in a preferred embodiment of the present invention.
[0046] Figure 3 This is a schematic diagram of an automatic vehicle safety warning control system according to a preferred embodiment of the present invention. Detailed Implementation
[0047] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein.
[0048] Please refer to Figure 1 In a preferred embodiment, a vehicle safety warning automatic control method includes:
[0049] S100: When the vehicle is in motion, acquire the forward image information of the current vehicle.
[0050] S200: When a warning target is detected in the image information, the vehicle warning sound control program is entered; the warning target includes at least: sharp bends and horn signs.
[0051] S300: Calculate the distance between the current vehicle and the warning target.
[0052] S400: When the distance meets the preset conditions, control the current vehicle to issue a safety warning according to the preset rules.
[0053] In practice, the system acquires the image information of the vehicle's front using a front-mounted camera, identifies whether a warning target exists in the image information using an embedded target recognition model, calculates the distance between the vehicle and the warning target using an embedded distance calculation model, and judges the distance between the vehicle and the warning target using a judgment module. This judgment module can be connected to the vehicle's warning sound system. When the system detects that the vehicle is on a sharp bend with a horn sign, the algorithm automatically controls the vehicle's warning sound system to sound the horn to remind oncoming vehicles, thereby reducing the occurrence of accidents.
[0054] In a variation of the above embodiment, the identification and judgment of sharp bends and horn signs are achieved through a target recognition model and a distance calculation model. This breaks away from the limitations of existing technologies that only identify single traffic light targets, increases the judgment factors of the assisted driving vehicle, and reduces the driver's reliance on their own judgment through algorithms, thereby reducing latency and the risk of false alarms.
[0055] Further understanding reveals that, since the judgment module is connected to the vehicle's safety warning system, it automatically controls the activation and deactivation of the horn warning function. This avoids disorderly horn use and reduces the need for signal generators installed on traffic lights at intersections, as is currently the case. By using algorithms to reduce reliance on external signal generators, the feasibility and difficulty of implementation are increased, the practicality and applicability of the calculation are improved, and the implementation cost is reduced, making it more worthy of promotion.
[0056] Please refer to Figure 2 In the above embodiments, step S200 specifically includes:
[0057] S201: Extract the target region from the image information using a warning target recognition model; the target region includes at least: traffic sign region and road region.
[0058] S202: Identify the target area. If the warning target exists, proceed to the vehicle warning sound control program; otherwise, return to S100.
[0059] In practice, the warning target recognition model is a neural network, which is a type of supervised learning. The characteristic of supervised learning is that each sample in the training sample set has a corresponding label, that is, each sample has its own corresponding category. The result obtained by the activation function of the neural network is actually the probability of which category the feature data belongs to. For example, if the training sample set has two categories: horn signs and sharp bends, after inputting the feature data into the neural network, the resulting category and the probability corresponding to the category are the probability of a certain horn sign and the probability of a certain sharp bend, respectively.
[0060] To understand this further, a sharp bend can be a road section with an angle greater than or equal to 90 degrees.
[0061] The extraction module in the warning target recognition module segments the acquired image information to extract the traffic sign area and road area, and then the recognition module identifies the target area to obtain the warning target.
[0062] In some embodiments, step S300 specifically includes:
[0063] S301: Extract the location information of the warning target through a distance calculation model; the location information includes at least: road width information, vehicle and camera angle information, and vehicle size information.
[0064] S302: Calculate the location information to obtain the first distance L1 between the current vehicle and the sharp bend road condition, and the second distance L2 between the current vehicle and the horn sign.
[0065] In the specific implementation process, the distance calculation model and the warning target recognition model are both neural networks. The relevant principles will not be elaborated here. It should be understood that the angle information between the vehicle and the camera can be obtained through the vehicle body level sensor.
[0066] In some embodiments, step S400 specifically includes:
[0067] S401: The vehicle safety warning sign F is the first sign.
[0068] S402: Set the first threshold L1' and the second threshold L2'.
[0069] S403: Determine whether the first distance L1 is less than the first threshold L1'. If so, trigger the vehicle safety warning function and set the reminder sign F to the second sign; otherwise, proceed to S402.
[0070] S404: Determine whether the second distance L2 is less than the second threshold L2'. If so, trigger the vehicle safety warning function and set the reminder flag F to the second flag; otherwise, return to S401. Wherein, L1' and L2' are real numbers greater than zero.
[0071] In some embodiments, step S400 further includes:
[0072] When the warning sign F is the second sign, the vehicle horn warning function will no longer be triggered, and steps S200 to S300 will be repeated.
[0073] When the reminder flag F is the second flag, and the warning target is not identified within a preset time Ts, the reminder flag is set to the first flag; where T is a real number greater than zero.
[0074] In specific implementation, this can be achieved by setting the automatic start-stop function of the vehicle's prompt sound system (or other safety warning system). For example, the first sign can be 0 and the second sign can be 1. When the detection algorithm identifies the horn sign and sharp bend during driving, it triggers the horn prompt function and sets the reminder sign F to 1. When F is 1 and a specified time Ts has elapsed, the reminder sign F is set to 0, and the vehicle enters the next horn detection waiting state. Furthermore, the automatic horn will not be triggered again while F is equal to 1. Steps S200 to S300 are repeated. If no sharp bend or horn sign is detected within the specified time Ts, the reminder sign F is set to 0.
[0075] In some embodiments, prior to step S100, the method further includes:
[0076] The warning target recognition model is obtained by training a neural network using data from horn signage and sharp bend road conditions.
[0077] The distance calculation model is obtained by training the neural network using road width data, vehicle and camera angle data, and vehicle size data.
[0078] In practice, the deep neural network can be any of the convolutional neural network, deep neural network, and backpropagation neural network, but is not limited to these.
[0079] As another preferred embodiment, the present invention also provides a system for automatic control of vehicle safety warnings, the system comprising at least:
[0080] The acquisition module is used to acquire the current image information of the vehicle in front when the vehicle is in motion.
[0081] The warning target recognition module is used to identify warning targets in the image information.
[0082] The distance calculation module is used to identify the distance between the current vehicle and the warning target.
[0083] The judgment module is used to determine whether the distance meets the preset conditions and control the current vehicle to sound its horn according to the preset rules.
[0084] In some embodiments, the warning target recognition module includes at least: a first extraction module for extracting a target region from the image information; and a recognition module for recognizing the target region to obtain the warning target.
[0085] In some embodiments, the distance calculation module includes at least: a second extraction module for extracting the location information of the warning target; and a calculation module for calculating the location information to obtain a first distance L1 between the current vehicle and the sharp bend road condition, and a second distance L2 between the current vehicle and the horn sign.
[0086] In some embodiments, the acquisition module is connected to a vehicle-mounted front-facing camera; the distance calculation module is connected to a vehicle level sensor, which is used to acquire angle information between the vehicle and the camera; the judgment module is connected to a vehicle-mounted safety warning system, which is equipped with a safety warning function; the acquisition module, the warning target recognition module, the distance calculation module, and the judgment module are embedded in the vehicle's infotainment system.
[0087] As another preferred embodiment, the present invention also provides a storage medium located in any control unit, the storage medium comprising a computer program executable by a processor, the computer program being used to perform the vehicle safety warning automatic control method as described above.
[0088] In summary, this invention utilizes algorithms to automatically sound the horn when identifying sharp bends and horn signs during driving, proactively reminding oncoming vehicles to drive safely. Simultaneously, it automatically controls the activation and deactivation of the horn warning function, avoiding disorderly horn use, enriching the application scenarios of the perception algorithm, reducing accidents caused by sharp bends, and improving driving safety.
[0089] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.
[0090] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.
[0091] The various system and method embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to embodiments of the present invention. The present invention can also be implemented as a system program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of functions is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple tools or components may be combined or integrated into another system, or some features may be ignored or not executed.
[0093] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0094] Although the invention has been described in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.
Claims
1. An automatic control method for vehicle safety warnings, characterized in that, The method includes: S100: When the vehicle is in motion, acquire the current image information of the front of the vehicle; S200: When a warning target is detected in the image information, the vehicle warning sound control program is entered; the warning target includes at least: sharp bends and horn-honking signs; S300: Calculate the distance between the current vehicle and the warning target; S400: When the distance meets the preset conditions, control the current vehicle to issue a safety warning according to the preset rules; Step S300 specifically includes: S301: Extract the location information of the warning target using a distance calculation model; the location information includes at least: road width information, vehicle and camera angle information, and vehicle size information; S302: Calculate the location information to obtain the first distance L1 between the current vehicle and the sharp bend road condition, and the second distance L2 between the current vehicle and the horn sign. Step S400 specifically includes: S401: Set the vehicle safety warning sign F as the primary sign; S402: Set the first threshold L1' and the second threshold L2'; S403: Determine whether the first distance L1 is less than the first threshold L1'. If so, trigger the vehicle safety warning function and set the reminder sign F to the second sign; otherwise, proceed to S402. S404: Determine whether the second distance L2 is less than the second threshold L2'. If so, trigger the vehicle safety warning function and set the reminder flag F to the second flag; otherwise, return to S401; where L1' and L2' are real numbers greater than zero. When the reminder sign F is the second sign, the vehicle horn warning function will no longer be triggered, and steps S200 to S300 will be repeated. When the reminder flag F is the second flag, and the warning target is not identified within a preset time Ts, the reminder flag is set to the first flag; where T is a real number greater than zero.
2. The automatic control method for vehicle safety warnings according to claim 1, characterized in that, Step S200 specifically includes: S201: Extract the target region from the image information using a warning target recognition model; the target region includes at least: a traffic sign region and a road region; S202: Identify the target area. If the warning target exists, proceed to the vehicle warning sound control program; otherwise, return to S100.
3. The automatic control method for vehicle safety warnings according to claim 2, characterized in that, Before step S100, the method further includes: The warning target recognition model is obtained by training a neural network using data from horn signage and sharp bend road conditions. The distance calculation model is obtained by training a neural network using road width data, vehicle and camera angle data, and vehicle size data.
4. A system employing the automatic vehicle safety warning control method as described in any one of claims 1-3, characterized in that, The system includes at least: The acquisition module is used to acquire the current image information of the vehicle in front when the vehicle is in motion; The warning target recognition module is used to identify warning targets in the image information; A distance calculation module is used to identify the distance between the current vehicle and the warning target; The judgment module is used to determine whether the distance meets the preset conditions and control the current vehicle to sound its horn according to the preset rules.
5. The system according to claim 4, characterized in that, The warning target recognition module includes at least: The first extraction module is used to extract the target region from the image information; And an identification module, used to identify the target area, thereby obtaining the warning target; The distance calculation module includes at least: The second extraction module is used to extract the location information of the warning target; And a calculation module, used to calculate the location information to obtain the first distance L1 between the current vehicle and the sharp bend road condition, and the second distance L2 between the current vehicle and the horn sign.
6. The system according to claim 5, characterized in that, The acquisition module is connected to the vehicle's front-facing camera; The distance calculation module and the vehicle level sensor are used to acquire the angle information between the vehicle and the camera. The judgment module is connected to the vehicle safety warning system, which is equipped with a safety warning function. The acquisition module, warning target recognition module, distance calculation module, and judgment module are embedded in the vehicle's infotainment system.
7. A storage medium, characterized in that, Located in any control unit, the storage medium includes a processor-executable computer program for performing the automatic vehicle safety warning control method as described in any one of claims 1-3.